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Parameters Range Reynolds number (re) Relative pitch ratio (p/e) Relative blockage height (e/h) Angle of attack (α) Open area ratio (β) 2000-20000 4-12 (Five Value) 0.8 (One Value) 60° (One Values) 20% (One Value) Data Collection The following parameters have been measured under the steady state conditions: (a) Temperatures of absorber plate, (b) Inlet air temperatures measured at the inlet section of test section, (c) Inlet air temperatures measured at the inlet section of test section, (d) Outlet air temperatures measured at the outlet section of test section, (e) Pressure drop across the test section (𝛥hd), (f) Pressure drop across the orifice plate (𝛥ho).
Besides, data with respect to temperature and pressure has also been collected for a smooth duct for comparison and determination of thermo hydraulic parameters of duct.
Data Reduction Under steady state conditions, the experimental data have been measured and recorded for given mass flow rate of air and heat flux.
Validity Test Values of Nusselt number and friction factor have been determined for smooth duct using experimental data collected and compared with the values predicted by Dittus Boelter Equation Nu=0.023×Re0.8×Pr0.4 and Modified Blasius Equation fs=0.085×Re-0.25.
The average absolute deviation between experimental and predicted values of Nusselt number and friction factor has been found to be 3.18% and 1.89% respectively which ensure the good accuracy in measurements of experimental data.

Using test enginery to record the data of load and displacement.
By Moore the coulomb's law, it is known that the pore water pressure of the existence of rock, reduce the effective stress of rock, but partial stress unchanged, the rocks easier to achieve ultimate strength, so the water pressure on rock fracture has the effect of should not be neglected. 3 Conclusion graph and data analysis of experimental results Rock specimens fixed stability, the experiment all ready, hydraulic press on blade, add water to 88 seconds hydraulic press pressure when the initial pressure to the requirements of 0.5 Mpa, and then in 113 seconds of rock testing machine results vertical displacement axial load, when loading to 321 seconds rock specimens suddenly craze, water bursting occurred phenomenon, water pressure falling rapidly.
Fig.3 Conclusion figure of preliminary data hydraulic press Fig.4 Conclusion figure of press preliminary data By press pressure data have to rock specimens subjected to stress data have to rock specimens stress-strain curve as shown in figure 5 shows: The experiment results of rock machine to the vertical axis of uniform loading, displacement to 350 micron rock specimens internal appear when tiny crack and testing machine pressure has dropped, appear stress the temporary reduction in the open loop concave curve.

The main advantage of S-shaped curve is that it catches most of the economic phenomenon’s growth essence and requires less data.
So it is suitable to forecast new industry’s development where the available data is very limit.
So it can depict the growth essence of EVs and requires a few data.
Because the EV in China is a new industry, and there has no historical data, it is a better policy to estimate the intrinsic growth rate of EVs based on that of ICEVs.
As we have seen, this model can catch the characteristics of EVs growth and need less data.

Therefore, the early detection and diagnosis of breast cancer is a very important procedure for mortality reduction.
The Otsu thresholding scheme first normalizes the histogram of the input data set as a probability distribution, and supposes that all elements of the set are divided into two classes by a threshold.
When performing experiments, data are frequently tabulated in the form of ordered pairs with each distinct.
Given the data, it is then usually desirable to be able to predict from by finding a mathematical model, that is, a function that fits the data as closely as possible.
One way to determine how well the function fits these order pairs is to measure the sum of squares of the errors (SSE) between the predicted values of y and the observed values for all of the n data points.

The observed peaks at 2q = 22, 27, 37 more or less matched with the standard JCPDS data (File no.37-0917) which corresponds to the planes (2 2 0), (1 0 1) and (0 4 1) respectively.
It was generally known that the polycrystalline films result in reduction of surface roughness.
The films with thickness 450nm show better results which is in good agreement with the XRD data.
The data on morphological changes due to annealing of films at different temperature is not available at this stage.
To correlate the morphology with structural and optical studies, further data on more films with varying thickness is needed.

Introduction Data reported in traffic show that road circulation is getting more and more agglomerated and slow, favoring production of numerous congestions.
Fig. 2 Phase plan utilized Data collected from traffic by students during peak hours show very high volumes as follows: Fig. 3 Data collected where v denotes the traffic volumes These data were collected on May 13, 2014, for interval 1600- 1700.
The process is described below: Fig. 4 Data processed in Webster method where s is the symbol for saturation flow.
During the peak hours, we stopped the access from north, meaning we removed phase IV and tried again to apply Webster method: Fig. 8 Data processed with Webster method for three phases signal plan This time, the value determined for cycle length is 167, 264.
On the other hand, scenario S4 offers the best results (reductions of at least 16 seconds on all streets) and generates only 30 seconds delay for south access.

In this study the acoustic emission sensors are used to collect the valve failure signal from a test loop, and the collected data is analyzed with the TFR's.
Then the data collected and analyzed is used for the diagnostic process.
The root mean square (rms) value, correlation, coherence, power spectral density (PSD), modified amplitude probability distribution (MAPD) slope, etc. are typical analysis data.
The sampling rate is 1 [MHz], and it is decided based on the past experience data.
In addition, an accelerometer and an ultrasonic sensor are used in parallel to verify the AE sensor data detected.

Table 9 Loss of water changes of optimal formula Time(min) 0 0.5 1.0 2.0 5.0 7.5 10.0 15.0 25.0 Loss of water (ml) 0 10.0 13.1 20.0 22.1 25.3 26.2 27.5 30.2 Data in Table 9 show that loss of water in 10 min reached 26.2mL, which indicates superior performance.
Table 10 The over time changes for material loading situation of optimal formula Time(min) 0 5 10 15 20 25 30 35 40 Force (kN) 0 7.5 15 22.5 30.0 37.5 45 52.5 60 Data in Table 10 show that time is proportional to the force of the material under the formula.
Table 11 The over time changes for thickening degree of optimal formula Time(min) 0 50 100 150 200 250 251 252 253 The thickening degree (%) 0 5.1 6.2 6.7 6.9 7.1 40.2 97.68 97.67 Data in Table 11 show that, during the first 250 min, the thickening degree of the material is not high, and thickening process was relatively slow.
Improvement on Data Processing in L9 (34) Orthogonal Design and Excel Application.
The establishment of data-autocalculator system for orthogonal experiment by Microsoft Excel.

The relationship between urbanization and energy consumption has been extensively studied in recent decades, using various types of data and models at the national, city and household level.
Variables and Data Discussion The urbanization process is mainly that population and non-agricultural economic activity concentrate in urban areas and urban density increases, thereby influencing household energy use patterns [10],[11].
Since the nineties of last century, China's urbanization level has saw from stable development to rapid development, so the data of 1990-2010 is used.
These data should be processed before used.
In order to eliminate heteroscedasticity and violent fluctuation of the data [14], and to facilitate short and long term analysis of the variables, this paper uses the natural logarithm of energy consumption and urbanization rate, ln(Tect), ln(Cityt), ln(Coalt), ln(Oilt), ln(Gast) and ln(Powert) as the variables[15].

This data provides information that the MIP caffeine sample has a smaller concentration than the NIP.
Table 1 shows different transmittance data (in %) between NIP and MIP.
Transmittance data from NIP and caffeine MIP.
The analysis of the obtained data can be seen in Table 2.
Summary FTIR data reveals that the concentration of caffeine decreases after the extraction process.